Credit card fraud is one example of fraud. Credit card fraud poses significant risk and financial exposure not only to the issuing financial institution but also to the victimized merchants. In addition to financial exposure, the reputation and trust of the issuing card brand and bank can be adversely impacted. Such fraud also can cause significant inconvenience and distress for those individuals whose financial information is compromised.
Many financial transactions are e-commerce or Point of Sale (POS) transactions which are transmitted electronically. Although this is of benefit to consumers this also enables stolen credit card information to be processed by organized and technically proficient criminals in large quantities as a compromised e-commerce site or POS can allow a credit card fraud ring to electronically intercept thousands of the credit card numbers, security codes and card holder identities.
In a typical organized credit card information theft event, a criminal organization or individual through the use of malware or network intrusion will compromise the client (customer), retailer (seller) or financial institution to gain as many individual credit card identities as possible. These criminal organizations will then either sell the information or use it themselves to conduct as many transactions as possible with the information they have acquired across all the different accounts until the financial institution detects the activity and blocks the accounts.
As we have moved to an ecommerce online driven retail system, the front line defense of the fraudster actually having to produce a card and conduct the transaction in person is now removed. Credit card rings can conduct all their activities online where the only validation methods present is the information that was compromised: account number, security code and the personal information of the account holder.
Armed with this information, an organized credit card fraud ring can conduct thousands of transactions in a very short period of time before the financial institution or the credit card holder becomes aware of the activity. Because of the speed at which the information can be stolen and used, exposure to the retailer and the financial institution is calculated in minutes not days. Rapid detection is vital as the earlier a pattern the detected the less the exposure.
Traditionally, credit card issuers' primary defense against credit card fraud has been through “fraud modeling” where typical fraudulent behavior is analyzed and encapsulated in a series of algorithms which the fraud model utilizes to judge the risk of the transaction being presented in the flow. This method is relied on due the rapidly increasing numbers transactions as society has gravitated toward electronic payment and away from cash or check transactions even for relatively small purchases.
Fraud models however do not learn fraud trends. Rather the systems must be taught the patterns which are indicative of fraud. As credit card fraud rings become increasingly technically proficient they also learn to adapt their patterns of transactions in an attempt to circumvent the fraud modeling rules. Financial institutions therefore have to rely on continual analysis of transactions to detect new and emerging trends in order to establish and build new rules which to push to the fraud engine.
The task posed to the financial institution and the retailer is finding these emerging trends as fast as possible to limit their exposure. The longer the amount of time it takes for the financial institution to detect a fraud trend the more money that is being lost to the fraud and in the electronic age, minutes can mean thousands of dollars in credit card fraud losses.
While it is a relatively routine process to determine patterns in known fraudulent transactions, this requires that enough transactions for an accurate analysis have already been identified as fraud losses before any preventative steps can be taken to deter the activity. There is a therefore a great need by financial institutions, card issuers and associations for proactive identification of immerging trends, decreasing the time to discovery to minimize risk.
Further, the need to detect of errors in data entry relating to financial transactions is not limited to detection of credit card fraud. Although many data entry processes have been automated, a significant numbers of transactions still require manual entries to be made into back-office financial/business administration systems.
Due to the high number of entries in such systems, and the interrelatedness of the different general ledger accounts in the double entry bookkeeping system, mistakes result in unbalanced entry books. Unbalanced entry books or in general the business administration data can then lead to errors in balance sheets or financial results.
Thus for example a loan entry might erroneously be entered in the books as an asset, when it is a liability leading to errors in the evaluation of a company. Similarly, if the entry protocol for entering data relating to a particular sales or manufacturing process is not followed properly it may not be clear if there has been a profit or a loss, what potentially the impact is on the stock levels etc. Identifying and correcting such errors can be very time consuming.
In view of the above an analysis system is desirable which assists with the identification of erroneous or suspect transactions, entries or events.